Affordance Learning for End-to-End Visuomotor Robot Control. - Aleksi Hämäläinen; Karol Arndt; Ali Ghadirzadeh; Ville Kyrki (IROS, 2019)

Meta Reinforcement Learning for Sim-to-real Domain Adaptation. - Karol Arndt; Murtaza Hazara; Ali Ghadirzadeh; Ville Kyrki (ICRA, 2020)

Domain Curiosity: Learning Efficient Data Collection Strategies for Domain Adaptation. - Karol Arndt; Oliver Struckmeier; Ville Kyrki (IROS, 2021)

Affine Transport for Sim-to-Real Domain Adaptation. - Anton Mallasto; Karol Arndt; Markus Heinonen; Samuel Kaski; Ville Kyrki (CoRR, 2021)

Few-Shot Model-Based Adaptation in Noisy Conditions. - Karol Arndt; Ali Ghadirzadeh; Murtaza Hazara; Ville Kyrki (IEEE Robotics Autom. Lett., 2021)

Online vs. Offline Adaptive Domain Randomization Benchmark. - Gabriele Tiboni; Karol Arndt; Giuseppe Averta; Ville Kyrki; Tatiana Tommasi (HFR, 2022)

Training and Evaluation of Deep Policies Using Reinforcement Learning and Generative Models. - Ali Ghadirzadeh; Petra Poklukar; Karol Arndt; Chelsea Finn; Ville Kyrki; Danica Kragic; Mårten Björkman (J. Mach. Learn. Res., 2022)

SafeAPT: Safe Simulation-to-Real Robot Learning Using Diverse Policies Learned in Simulation. - Rituraj Kaushik; Karol Arndt; Ville Kyrki (IEEE Robotics Autom. Lett., 2022)

Co-imitation: Learning Design and Behaviour by Imitation. - Chang Rajani; Karol Arndt; David Blanco Mulero; Kevin Sebastian Luck; Ville Kyrki (AAAI, 2023)

Beyond invariant representation learning: linearly alignable latent spaces for efficient closed-form domain adaptation. - Oliver Struckmeier; Ievgen Redko; Anton Mallasto; Karol Arndt; Markus Heinonen; Ville Kyrki (CoRR, 2023)

Understanding deep neural networks through the lens of their non-linearity. - Quentin Bouniot; Ievgen Redko; Anton Mallasto; Charlotte Laclau; Karol Arndt; Oliver Struckmeier; Markus Heinonen; Ville Kyrki; Samuel Kaski (CoRR, 2023)

DROPO: Sim-to-real transfer with offline domain randomization. - Gabriele Tiboni; Karol Arndt; Ville Kyrki (Robotics Auton. Syst., 2023)

Learning representations that are closed-form Monge mapping optimal with application to domain adaptation. - Oliver Struckmeier; Ievgen Redko; Anton Mallasto; Karol Arndt; Markus Heinonen; Ville Kyrki (Trans. Mach. Learn. Res., 2023)

From Alexnet to Transformers: Measuring the Non-linearity of Deep Neural Networks with Affine Optimal Transport. - Quentin Bouniot; Ievgen Redko; Anton Mallasto; Charlotte Laclau; Oliver Struckmeier; Karol Arndt; Markus Heinonen; Ville Kyrki; Samuel Kaski (CVPR, 2025)